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The deeper analysis of our data yields surprises, and
sometimes even requires re-examination of notions
built up over decades of research at the edges of
what we know about consciousness. Peter
Bancel has been looking at the timecourse of deviations
associated with the formal events. One aspect of the picture
is a somewhat surprising, albeit subtle suggestion that "what goes up must
come down." The most recent (mid-2007) analyses
addressing this issue show clear indications of a
kind of "recovery" phase in addition to the original effect
on the data associated with major events. At first glance
this looks like there must be some strange tendency for
nature to remember the deviations from randomness and
compensate for them. But such an accounting is not in the
nature of mother nature, or more specifically, it is not in the nature
of randomness. The expectation for a random sequence
unaffected by some influence is that
the next element is unpredictable. That implies that
wherever the sequence is now is always the starting point
for the future sequence -- there is no influence of past
history.
But the remarkable findings of this analysis, shown in the
graphs below, do require some
explanation. If it isn't the world pushing back against an
anomaly, what might cause this symmetrical reversal of
trend? I think the most
likely explanation is differences in the state or quality
of "global consciousness" over the time course of a
major event. It isn't hard to imagine that at the beginning
of a major event its newness and our surprise might dominate
our presence, and as we understand more or progress in our
experience, the shared perception would also evolve. For
example, a terrorist attack will arouse shock and
astonishment, and most likely fear at first, perhaps the
beginnings of anger. And then we may
shift to feelings of deep concern and compassion for those
hurt and eventually for their loved ones. Later, the intensity
and depth of feeling must transform into
profound sadness and a worldly fatigue.
As we learn more about the psychological and
social context of the effects, and about other possible
influences, the picture may become more clear.
The first figure shows the data during the event in blue,
and data from the subsequent similar period
in red.
The measure is a combination of Netvar and Covar which is
called the Dispersion Statistic, and the plot shows the
cumulative deviation of the statistic from expectation.
The data from all events amenable to this analysis (N=187)
are used.
The second figure presents the same statistics in a
simple time sequence. In this case four segments are shown
in color to allow easy identification. The first red segment
is a pre-event period of the same length as the defined
event. The first blue segment is the event itself. Then we
see the post event "recovery" period and finally another
segment of the same length. Both the first and last segments
are unexceptional random walks, but the event and the
post-event trends are strongly deviant, and the slopes are
of opposite sign. The trend during the event
is a little longer, but both are significant.
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